Machine Learning
Machine learning
ML models integrate with rules, add explainability, monitor drift, and deploy quickly with compliance guardrails.
Put machine learning into production
Enterprises struggle when models remain stuck in experiments, lack explainability, or drift over time. InRule ML Studio simplifies deployment with one-click deployment pipelines, provides explainability for transparency and compliance, and continuously monitors model performance to keep outcomes accurate and auditable.
One-click deployment
Publish models as REST APIs without complex coding or DevOps.
Explainability
Provide feature-level insights so predictions are transparent and auditable.
Drift monitoring
Detect accuracy issues and shifts in data over time to keep models reliable.
Support has always been outstanding. It is very refreshing to work with a company that is focused on solving problems.
– VP Information Systems, Residential Earthquake Insurance Company (insurance)
Machine learning in practice
Train models
Build and train models using InRule ML Studio or bring your own pre-trained models.
Validate accuracy
Test models with representative data to confirm they deliver reliable outcomes.
Deploy pipelines
Publish models as REST APIs through automated pipelines without heavy coding or DevOps.
Explain predictions
Use feature-level insights and clustering visualizations to make predictions transparent and auditable.
Monitor drift
Automatically track performance and detect drift to keep models accurate and compliant.
Integrate models into decision flows
Models sit alongside rules so predictions remain under control.
Models integrate directly into decision flows. Predictions never operate on their own — rules always govern how outputs are used, keeping decisions explainable and policy compliant.
Meet regulations
Enforce guardrails for responsible use
Fallback logic and thresholds minimize risk and bias.
Deterministic constraints keep predictions explainable and safe, especially for regulated decisions with high impact.
Understand model behavior with explainable cohort insights
Cohort analysis reveals which features drive differences between groups.
It provides a whole-model view that uncovers patterns and drivers across your population—not a single-prediction explanation. With clear, ranked drivers and side-by-side cohort comparisons, teams can validate model logic, surface segments at risk of unfair outcomes, and confidently communicate the rationale to business stakeholders, auditors, and regulators—turning insights into better policies and more consistent decisions.”
Meet regulations
Automate deployment pipelines with regulatory oversight
One-click deployment publishes models as REST APIs without heavy DevOps.
InRule delivers training-to-endpoint deployment without complex scripting. Unlike Azure, AWS, or GCP, pipelines are automated so teams scale learning quickly and compliantly.
Resources
Explore the platform in action
Explore real-world examples of how organizations use InRule to unify rules, processes, and data. Read articles, watch webinars, and access expert insights on building transparent, adaptable automation.
